SharkNet Networks Applications in Smart Manufacturing Using IoT and Machine Learning

Data Availability Statement: The necessary research data have been presented in the article.With the advancement of Industry 4.0, 3D printing has become a critical technology in smart manufacturing; however, challenges remain in the integrated management, quality control, and remote monitoring of multiple 3D printers. This study proposes an intelligent cloud monitoring system based on the SharkNet dynamic network, IoT, and artificial neural networks (ANNs). The system utilizes a SharkNet dynamic network to integrate low-cost sensors for environmental monitoring to enable low-latency data transmission and deploys ANN models on the cloud for print quality prediction and process parameter optimization. Next, we experimentally validated the system using the Taguchi design and ANN-based analysis, focusing on optimizing printing process parameters and improving surface quality. The main results show that the designed system has a communication delay of 40–50 ms and 99.8% transmission reliability under moderate load, and the system reduces the surface roughness prediction error to less than 17.2%. In addition, the ANN model outperforms conventional methods in capturing the nonlinear relationships of the variables, and the system can be based on the model to improve print quality and productivity by enabling real-time parameter adjustments. The system retains a high degree of scalability in terms of real-time monitoring and parallel or complex control of multiple devices, which demonstrates its potential for applications in smart manufacturing.This research was funded by the Graduate Student Innovation Program of Shanxi Province, Grant No. 2023SJ214. It was also partly funded by Brunel University London

Evaluation of Machine Learning and Traditional Statistical Models to Assess the Value of Stroke Genetic Liability for Prediction of Risk of Stroke Within the UK Biobank

Data Availability Statement: The data used in this study is available on request from the UK Biobank.Acknowledgments: This research was conducted using the UK Biobank under Application Number 60549 (www.ukbiobank.ac.uk (accessed on 5 February 2021)). The UK Biobank is generously supported by its founding funders, the Wellcome Trust and the UK Medical Research Council, as well as by the British Heart Foundation, Cancer Research UK, the Department of Health, the Northwest Regional Development Agency, and the Scottish Government. The MEGASTROKE project received funding from sources specified at https://megastroke.org/acknowledgements.html (accessed on 13 September 2022).Supplementary Materials are available online at: https://www.mdpi.com/2227-9032/13/9/1003#app1-healthcare-13-01003 .Background and Objective: Stroke is one of the leading causes of mortality and long-term disability in adults over 18 years of age globally, and its increasing incidence has become a global public health concern. Accurate stroke prediction is highly valuable for early intervention and treatment. There is a scarcity of studies evaluating the prediction value of genetic liability in the prediction of the risk of stroke. Materials and Methods: Our study involved 243,339 participants of European ancestry from the UK Biobank. We created stroke genetic liability using data from MEGASTROKE genome-wide association studies (GWASs). In our study, we built four predictive models with and without stroke genetic liability in the training set, namely a Cox proportional hazard (Coxph) model, gradient boosting model (GBM), decision tree (DT), and random forest (RF), to estimate time-to-event risk for stroke. We then assessed their performances in the testing set. Results: Each unit (standard deviation) increase in genetic liability increases the risk of incident stroke by 7% (HR = 1.07, 95% CI = 1.02, 1.12, p-value = 0.0030). The risk of stroke was greater in the higher genetic liability group, demonstrated by a 14% increased risk (HR = 1.14, 95% CI = 1.02, 1.27, p-value = 0.02) compared with the low genetic liability group. The Coxph model including genetic liability was the best-performing model for stroke prediction achieving an AUC of 69.54 (95% CI = 67.40, 71.68), NRI of 0.202 (95% CI = 0.12, 0.28; p-value = 0.000) and IDI of 1.0 × 10−4 (95% CI = 0.000, 3.0 × 10−4; p-value = 0.13) compared with the Cox model without genetic liability. Conclusions: Incorporating genetic liability in prediction models slightly improved prediction models of stroke beyond conventional risk factors.This research received no external funding

Collecting real-time infant feeding and support experience: co-participatory pilot study of mobile health methodology

Data availability: The quantitative dataset supporting the conclusions of this article is available in the OSF project https://osf.io/yqsnd/ [https://doi.org/10.17605/OSF.IO/YQSND].Supplementary Information is available online at: https://internationalbreastfeedingjournal.biomedcentral.com/articles/10.1186/s13006-025-00707-7#Sec31 .Background: Breastfeeding rates in the UK have remained stubbornly low despite long-term intervention efforts. Social support is a key, theoretically grounded intervention method, yet social support has been inconsistently related to improved breastfeeding. Understanding of the dynamics between infant feeding and social support is currently limited by retrospective collection of quantitative data, which prohibits causal inferences, and by unrepresentative sampling of mothers. In this paper, we present a case-study presenting the development of a data collection methodology designed to address these challenges. Methods: In April–May 2022 we co-produced and piloted a mobile health (mHealth) data collection methodology linked to a pre-existing pregnancy and parenting app in the UK (Baby Buddy), prioritising real-time daily data collection about women's postnatal experiences. To explore the potential of mHealth in-app surveys, here we report the iterative design process and the results from a mixed-method (explorative data analysis of usage data and content analysis of interview data) four-week pilot. Results: Participants (n = 14) appreciated the feature’s simplicity and its easy integration into their daily routines, particularly valuing the reflective aspect akin to journaling. As a result, participants used the feature regularly and looked forward to doing so. We find no evidence that key sociodemographic metrics were associated with women’s enjoyment or engagement. Based on participant feedback, important next steps are to design in-feature feedback and tracking systems to help maintain motivation. Conclusions: Reflecting on future opportunities, this case-study underscores that mHealth in-app surveys may be an effective way to collect prospective real-time data on complex infant feeding behaviours and experiences during the postnatal period, with important implications for public health and social science research.We acknowledge the funding by the BA/Wellcome Trust small grants for supporting this project (reference SRG2021/210128)

PARTY FINANCE: LABOUR EXPLOITS ITS ADVANTAGE

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Neural Combinatorial Optimization for Multiobjective Task Offloading in Mobile Edge Computing

Task offloading is crucial in supporting resource-intensive applications in mobile edge computing. This paper explores multiobjective task offloading, aiming to minimize energy consumption and latency simultaneously. Although learning-based algorithms have been used to address this problem, they train a model based on one a priori preference to make the offloading decision. When the preference changes, the trained model may not perform well and needs to be retrained. To address this issue, we propose a neural combinatorial optimization method that combines an encoder-decoder model with reinforcement learning. The encoder captures task relationships, while the decoder, equipped with a preference-based attention mechanism, determines offloading decisions for various preferences. Additionally, reinforcement learning is employed to train the encoder-decoder model. Since the proposed method can infer the offloading decision for each preference, it eliminates the need to retrain the model when the preference changes, thus improving real-time performance. Experimental studies demonstrate the effectiveness of the proposed method by comparison with three algorithms on instances of different scales.10.13039/501100001809-National Natural Science Foundation of China (Grant Number: U23A20347); Royal Society International Exchange (Grant Number: IEC-NSFC-211264)

Association between total daily sedentary time and cardiometabolic biomarkers in older adults: A systematic review and meta-analysis

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UK Live Comedy Sector Survey Report 2024

The UK Live Comedy Sector Survey 2024 was jointly conducted by the Centre for Comedy Studies Research at Brunel University, the Live Comedy Association, and British Comedy Guide. The UK Live Comedy Sector Survey was administered by Brunel University of London and ethical approval to conduct the survey was received from the College of Business, Arts and Social Sciences Research Ethics Committee at Brunel University of London.This report outlines the main findings of the UK Live Comedy Sector Survey 2024 conducted by the Centre for Comedy Studies Research (CCSR), the Live Comedy Association (LCA) and British Comedy Guide (BCG). Until now very little was known about the size, scale and impact of the UK live comedy sector. The survey provides detailed insights about the economics of the live comedy sector including its size and its longevity, numbers of shows and ticket sales, and turnover. It also provides insights into regional variations, venues used and performance types supported, and reveals inequalities and inequities prevalent in the sector. The survey serves to support and advocate live comedy in the UK politically, economically and socially. 366 people working in UK live comedy completed the survey. 67% of respondents were comedians. 33% of respondents were people working as comedy promoters, producers, venue managers, festival organisers or agentsLive Comedy Association; Brunel University of London. Centre for Comedy Studies Research (CCSR); British Comedy Guide

An Impulsive Approach to State Estimation for Multirate Singularly Perturbed Complex Networks Under Bit Rate Constraints

In this article, the problem of ultimately bounded state estimation is investigated for discrete-time multirate singularly perturbed complex networks under the bit rate constraints, where the sensor sampling period is allowed to differ from the updating period of the networks. The facilitation of communication between sensors and the remote estimator through wireless networks, which are subject to bit rate constraints, involves the use of a coding-decoding mechanism. For efficient estimation in the presence of periodic measurements, a specialized impulsive estimation method is developed, which aims to carry out impulsive corrections precisely at the instants when the measurement signal is received by the estimator. By employing the iteration analysis method under the impulsive mechanism, a sufficient condition is established that ensures the exponential boundedness of the estimation error dynamics. Furthermore, an optimization algorithm is introduced for addressing the challenges related to bit rate allocation and the design of desired estimator gains. Within the presented theoretical framework, the correlation between estimation performance and bit rate allocation is elucidated. Finally, a simulation example is provided to demonstrate the validity of the proposed estimation approach.10.13039/501100019033-Key Area Research and Development Program of Guangdong Province (Grant Number: 2021B0101410005); 10.13039/501100003453-Natural Science Foundation of Guangdong Province of China (Grant Number: 2021A1515011634 and 2021B1515420008); 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: U22A2044 and 62206063); Local Innovative and Research Teams Project of Guangdong Special Support Program of China (Grant Number: 2019BT02X353); 10.13039/501100004543-China Scholarship Council (Grant Number: 202208440312)

Training Latency Minimization for Model-Splitting Allowed Federated Edge Learning

To alleviate the shortage of computing power faced by clients in training deep neural networks (DNNs) using federated learning (FL), we leverage the edge computing and split learning to propose a model-splitting allowed FL (SFL) framework, with the aim to minimize the training latency without loss of test accuracy. Under the synchronized global update setting, the latency to complete a round of global training is determined by the maximum latency for the clients to complete a local training session. Therefore, the training latency minimization problem (TLMP) is modelled as a minimizing-maximum problem. To solve this mixed integer nonlinear programming problem, we first propose a regression method to fit the quantitative-relationship between the cut-layer and other parameters of an AI-model, and thus, transform the TLMP into a continuous problem. Considering that the two subproblems involved in the TLMP, namely, the cut-layer selection problem for the clients and the computing resource allocation problem for the parameter-server are relative independence, an alternate-optimization-based algorithm with polynomial time complexity is developed to obtain a high-quality solution to the TLMP. Extensive experiments are performed on a popular DNN-model EfficientNetV2 using dataset MNIST, and the results verify the validity and improved performance of the proposed SFL framework.10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62132004); Jiangsu Major Project on Basic Researches (Grant Number: BK20243059)

Unlocking Business Success: How Networking and Branding Capabilities Drive Performance Through Product Innovativeness

Data Availability Statement: The data that support the findings of this study are available from the corresponding author upon reasonable request.In today's fast-paced market, developing innovative products with significant advantages over existing alternatives is essential for a strong market presence. This study, based on the resource-based and dynamic capability view, examines how market and technological innovativeness contribute to differentiation advantage and improved business performance. It also investigates the roles of complementary capabilities in enhancing these relationships. Primary data were collected through an on-site questionnaire survey of Iranian research and development-intensive manufacturing firms. Using 125 valid responses from senior managers, partial least squares structural equation modeling tested the proposed model. Findings indicate that networking and branding capabilities enhance technological and market innovativeness, respectively, thereby strengthening differentiation advantage. Moreover, differentiation advantage is a crucial mechanism for translating innovativeness into improved business performance. These results provide theoretical insights and practical guidance for developing effective product innovativeness strategies to augment international competitiveness and performance